Lixu LI

Dr. Lixu LI


Research Focus & Metrics

Academic Career Length

26Years (2001-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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16 December 2021 in International Journal of Production Economics
Verified
Journal Article
Closed access
FYFei Ye
Fei Ye
KLKe Liu
Ke Liu
LLLixu LI
Lixu LI
KLKee-Hung Lai
Kee-Hung Lai
YZYuanzhu Zhan
Yuanzhu Zhan
AKAjay Kumar
Ajay Kumar

27 July 2022 in Information & Management
Verified
Journal Article
Closed access
LLLixu LI
Lixu LI
YTYang Tong
Yang Tong
LWLong Wei
Long Wei
SYShuili Yang
Shuili Yang

1 January 2020 in Transportation Research Part D: Transport and Environment
Verified
Journal Article
Closed access
LLLixu LI
Lixu LI
ZWZhiqiang Wang
Zhiqiang Wang
LCLujie Chen
Lujie Chen
ZWZixuan Wang
Zixuan Wang

17 May 2022 in Journal of Business Research
Verified
Journal Article
Closed access
LLLixu LI
Lixu LI
FYFei Ye
Fei Ye
YZYuanzhu Zhan
Yuanzhu Zhan
AKAjay Kumar
Ajay Kumar
FSFrancesco Schiavone
Francesco Schiavone
YLYina Li
Yina Li

1 August 2019 in Journal of Environmental Management
Verified
Journal Article
Closed access
LLLixu LI
Lixu LI
FYFei Ye
Fei Ye
YLYina Li
Yina Li
CCChing-Ter Chang
Ching-Ter Chang

24 October 2022 in International Journal of Operations & Production Management
Verified
Journal Article
Closed access
LLLixu LI
Lixu LI
Yeming GongYeming Gong
Yeming Gong
ZWZhiqiang Wang
Zhiqiang Wang
SLShan Liu
Shan Liu
Purpose: Although big data may enhance the visibility, transparency, and responsiveness of supply chains, whether it is effective for improving supply chain performance in a turbulent environment, especially in mitigating the impact of COVID-19, is unclear. The research question the authors addressed is: How do logistics firms improve the supply chain performance in COVID-19 through big data and supply chain integration (SCI)?Design/methodology/approach: The authors used a mixed-method approach with four rounds of data collection. A three-round survey of 323 logistics firms in 26 countries in Europe, America, and Asia was first conducted. The authors then conducted in-depth interviews with 55 logistics firms.Findings: In the first quantitative study, the authors find mediational mechanisms through which big data analytics technology capability (BDATC) and SCI influence supply chain performance. In particular, BDATC and SCI are two second-order capabilities that help firms develop three first-order capabilities (i.e. proactive capabilities, reactive capabilities, and resource reconfiguration) and eventually lead to innovation capability and disaster immunity that allow firms to survive in COVID-19 and improve supply chain performance. The results of the follow-up qualitative analysis not only confirm the inferences from the quantitative analysis but also provide complementary insights into organizational culture and the institutional environment.Originality/value: The authors contribute to supply chain risk management by developing a three-level hierarchy of capabilities framework and finding a mechanism with the links between big data and big disaster. The authors also provide managerial implications for logistics firms to address the new management challenges posed by COVID-19.